TY - GEN
T1 - A New Semantic Segmentation Technique for Interference Mitigation in Automotive Radar
AU - Elsharkawy, Ahmed A.
AU - Abdallah, Abdallah S.
AU - Fakhr, Mohamed W.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Recent autonomous vehicles and Advanced Driver Assistance Systems (ADAS) are equipped with several sensing technologies, including cameras, LiDAR, radar, and ultrasonic. Due to its exceptional features, radar is increasingly utilized in a range of ADAS applications. Unfortunately, this increases the likelihood of radar-to-radar interference, which hinders radar functionality. Numerous research studies have investigated interference mitigation using various traditional signal processing or deep learning techniques. This paper presents a new technique utilizing the U-Net deep neural network (DNN) model for interference mitigation via semantic segmentation in such ADAS scenarios. By comparing the performance of the proposed model to previously published deep-learning-based approaches, our new model has demonstrated promising improvements based on standard evaluation criteria.
AB - Recent autonomous vehicles and Advanced Driver Assistance Systems (ADAS) are equipped with several sensing technologies, including cameras, LiDAR, radar, and ultrasonic. Due to its exceptional features, radar is increasingly utilized in a range of ADAS applications. Unfortunately, this increases the likelihood of radar-to-radar interference, which hinders radar functionality. Numerous research studies have investigated interference mitigation using various traditional signal processing or deep learning techniques. This paper presents a new technique utilizing the U-Net deep neural network (DNN) model for interference mitigation via semantic segmentation in such ADAS scenarios. By comparing the performance of the proposed model to previously published deep-learning-based approaches, our new model has demonstrated promising improvements based on standard evaluation criteria.
UR - http://www.scopus.com/inward/record.url?scp=85159782446&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85159782446&partnerID=8YFLogxK
U2 - 10.1109/WCNC55385.2023.10118913
DO - 10.1109/WCNC55385.2023.10118913
M3 - Conference contribution
AN - SCOPUS:85159782446
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023
Y2 - 26 March 2023 through 29 March 2023
ER -